NormalizationCatalog 클래스
정의
중요
일부 정보는 릴리스되기 전에 상당 부분 수정될 수 있는 시험판 제품과 관련이 있습니다. Microsoft는 여기에 제공된 정보에 대해 어떠한 명시적이거나 묵시적인 보증도 하지 않습니다.
숫자 정규화 구성 요소의 인스턴스를 만들기 위한 TransformsCatalog 확장 메서드 컬렉션입니다.
public static class NormalizationCatalog
type NormalizationCatalog = class
Public Module NormalizationCatalog
- 상속
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NormalizationCatalog
메서드
NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32) |
Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density. |
NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32) |
Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density. |
NormalizeGlobalContrast(TransformsCatalog, String, String, Boolean, Boolean, Single) |
Create a GlobalContrastNormalizingEstimator, which normalizes columns individually applying global contrast normalization.
로 |
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
NormalizeLpNorm(TransformsCatalog, String, String, LpNormNormalizingEstimatorBase+NormFunction, Boolean) |
Create a LpNormNormalizingEstimator, which normalizes (scales) vectors in the input column to the unit norm.
사용되는 norm의 형식은 .에 의해 |
NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data. |
NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data. |
NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data. |
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data. |
NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32) |
Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales the data according to the quantile range (defaults to the interquartile range). |
NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32) |
Create a NormalizingEstimator, which normalizes using statistics that are robust to outliers by centering the data around 0 (removing the median) and scales the data according to the quantile range (defaults to the interquartile range). |
NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32) |
Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the |
NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32) |
Create a NormalizingEstimator, which normalizes by assigning the data into bins based on correlation with the |
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